{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/loading-n-dimensional-vector-into-quantum","title":"Loading N-Dimensional Vector into Quantum Registers from Classical Memory with O(logN) Steps","arxiv_id":"quant-ph/0612061","date":"2006-12-08","proceeding":null,"authors":["Chao-Yang Pang"],"abstract":"Vector is the general format of input data of most algorithms. Designing unitary operation to load all information of vector into quantum registers of quantum CPU from classical memory is called quantum loading scheme (QLS). QLS assembles classical memory and quantum CPU as a whole computer, which will be important for further quantum computation. We present a QLS based on path interference with time complexity O(logN), while classical loading scheme has time complexity O(N), that is the efficiency bottleneck of classical computer.","url_abs":"https://arxiv.org/abs/quant-ph/0612061v3","url_pdf":"https://arxiv.org/pdf/quant-ph/0612061v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"loading-n-dimensional-vector-into-quantum","repo_url":"https://github.com/Arya-Bhatta/QOSF_Task_1","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"loading-n-dimensional-vector-into-quantum","repo_url":"https://github.com/AsishMandoi/qudratic-speedup-using-quantum","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}